Open Positions

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We are constantly looking for excellent and motivated PhD student and PostDoc candidates who want to join the Computational Systems Biology (CSB) group. The group develops computational methods for studying complex cellular networks to elucidate their operating principles and to enable their rational re-design. All projects involve close collaborations between experimental biologists and computer / systems scientists. Read more about research at CSB ...

Candidates should hold an advanced degree (MSc or PhD) in a quantitative (mathematics, computational sciences, engineering, or similar) discipline. He or she should have basic background in biology, and preferably prior experience in the mathematical / computational analysis or design of biological systems. The candidate must be able to communicate fluently in English (oral and written), and will be involved in multi- and interdisciplinary work in computational science and biology.

For specific open positions, please use the form below to send an application.

Available Positions:

(1) PhD: Multiscale modeling of influenza virus infection

The collaborative project VirX within, the Swiss initiative in Systems Biology, aims to generate a systems description of proteasomal processes involved in viral infections such as influenza infection. The quantitative model will bridge different levels from molecular mechanisms of virus uncoating in which proteasomal components are involved, ultimately to patients and their susceptibility to virus infection. The key challenge for development of mathematical models is to find appropriate descriptions at each level of organization (molecular, cellular, tissue, and patient) that can be systematically connected to enable predictions of virus infection and its dependence on detailed molecular mechanisms. In addition to model development in close collaborations with our project partners in biology and medicine, the work will therefore involve methods development for multiscale modeling in systems biology.

(2) PostDoc: Model-based analysis of single-cell behavior

Our increasing ability to measure behaviors and characteristics of single cells is currently not well matched with mathematical and computational methods for the analysis of the corresponding data. Fundamental questions such as, what are contributing factors to heterogeneity within populations of cells (e.g., stochastic molecular noise, heterogeneous micro-environments, un-synchronized cell growth) are unanswered. In close collaboration with internal and external experimental biologists, the project aims at devising methods for model-based data analysis for the scenario when individual cell behaviors over time as well as cell lineage information is available. This project requires a strong background in probability and statistics as well as dynamical systems theory. Prior experience with non-linear mixed effect models or similar, and their associated computational challenges is desirable.

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